(1) Field of the Invention
The present invention relates to a method for identifying regions in any intensity image (such as a mammogram) that correspond to an approximately known shape in which the regions stand out with a higher intensity with respect to a local background. The size of the region need only be known to within a range.
(2) Description of the Prior Art
It is known in the art that false negatives, those cancers missed during reading, can be attributed to the necessity of viewing large numbers of mammograms, the complex structure of the breast, the subtle nature of certain mammographic characteristics of breast cancer and fatigue or distraction. Some studies have indicated a potential increase of 4 to 15% in the number of breast cancers detected using a double reading of screening mammograms. Computer-aided detection is not intended to replace the medical judgment of a radiologist but has the potential to help the radiologist with a consistent, high level of attention in all cases (See J. Roehrig, and R. A. Castellino, “The Promise of Computer Aided Detection in Digital Mammography” European Journal of Radiology, 31, pp 35-39; 1999 and L. J. W. Burhenne et al., “Potential Contribution of Computer-Aided Detection to the Sensitivity of Screening Mammography” Radiology, 215(2), pp. 554-562, May 2000).
Computer-aided methods for detection of breast cancer often rely on artificial neural networks or ANNs (See I. Christoyianni, E. Dermatas, G. Kokkinakis, “Fast Detection of Masses in Computer-Aided Mammography,” IEEE Signal Processing Magazine, 17, pp 54-64, January 2000 and S. C. B Lo, H. Li, Y. Wang, L. Kinnard, M. T. Freedman, “A Multiple Circular Path Convolution Neural Network for Detection of Mammographic Masses,” IEEE Transactions on Medical Imaging, 21 (2), pp 150-158, February 2002; both incorporated herein by reference) These computer-aided methods require data for training in which the data must statistically match the actual mammogram data on which the computer-aided method is to be applied.
Artificial neural networks also involve nonlinear optimization, a computationally intensive procedure that does not guarantee achieving the best solution. Artificial neural networks thus utilize no statistical modeling and thus are capable of “overtraining”, i.e. achieving near perfect performance on the training set but poor generalization performance on the actual data to be tested. This overtraining cannot be controlled.
In Heine et al. (U.S. Pat. No. 6,310,967), there is disclosed a method for identifying calcifications in mammograms. The method uses wavelet decomposition to determine multi-resolution information by applying parametric or nonparametric (kernel based) statistical modeling to the wavelet decomposed from a training set. The models are then used to empirically determine thresholds for detection; however, identifying calcifications in a mammogram (a pre-cancerous condition) is a different type of detected abnormality than a mass (a cluster of cancer cells that keeps growing) in a mammogram.
In Lee et al. (U.S. Pat. No. 6,430,427), there is provided a method to estimate the trabecular index and bone mineral density. The focus of this deterministic method is on an estimation of a quantity rather than a region of concern.
In Seeger et al. (U.S. Pat. No. 6,577,762), there is provided a method to generate a background image of a pixmap image, which can then be used for additional image processing on the pixmap, such as identification of an image foreground. The determination of the background pixels in an image immediately determines the foreground pixels. The method uses empirical rules to determine the thresholds necessary for determining the background with the threshold surface a linear function of estimated background variances. The method does not directly account for the statistical nature of the background, specifically in distribution.
In Shiratani (U.S. Pat. No. 6,608,929), there is an apparatus for segmenting an image into plural regions according to feature (color) quantities of pixels. Use of image segmentation is different from detection of regions of interest from a background. For example, the regions of interest in mammograms are not homogeneous in intensity. Image segmentation algorithms are designed to segment such a region, not to identify it as a region of interest.
In view of the above, there is a need for a method to detect masses (potentially cancerous regions) in two-dimensional mammograms as abnormalities. The structure of the method can also be used to identify calcifications in mammograms. Specifically, the apriori choices of template sizes and shapes would decompose the image into regions of interest at various scales without the direct use of training sets. Nonparametric tolerance intervals would allow thresholds to be determined based on the current data in an image with the thresholds determined as a stochastic method by accounting for the statistical variability in the original space of pixel intensities, as opposed to wavelet transformed space.